5  Appendix B

5.1 Setup

5.1.1 Install Packages

We install the following packages using the groundhog package manager to increase computational reproducibility.

options(repos = c(CRAN = "https://cran.r-project.org")) 

if (!requireNamespace("groundhog", quietly = TRUE)) {
    install.packages("groundhog")
}

pkgs <- c("magrittr", "data.table", "stringr", "Rmisc", "ggplot2",
          "lmtest", "sandwich", "stargazer")

groundhog::groundhog.library(pkg = pkgs,
                             date = "2024-08-01")

rm(pkgs)

5.1.2 Read Data

data      <- readRDS(file="../data/processed/full.Rda")
timeSpent <- data.table::fread(file = "../data/raw/PageTimes-2021-09-15.csv")
raw       <- data.table::fread(file="../data/raw/all_apps_wide_2021-09-15.csv")

5.2 Table B.1

::: {#tbl-B-1 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E1 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E1 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E1 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E1",
            dep.var.caption = "Dependent variable: E1",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)

Linear regressions: Treatment effects on E1

	Dependent variable: E1
	full	confirmation	contradiction	point	interval	both
	(1)	(2)	(3)	(4)	(5)	(6)
contradiction	-0.962			-1.492	0.951	-2.435
	(1.080)			(1.760)	(1.993)	(1.855)
both		2.430	1.487
		(1.759)	(1.856)
interval		1.331	3.773**
		(1.836)	(1.923)
stage 2	-1.036	0.955	7.468***	0.955	-1.634	-2.504*
	(0.846)	(1.500)	(1.791)	(1.500)	(1.498)	(1.391)
contradiction x stage 2	7.563***			6.513***	4.269*	12.098***
	(1.315)			(2.336)	(2.232)	(2.251)
interval x stage 2		-3.459*	2.126
		(2.046)	(2.517)
both x stage 2		-2.589	-4.833**
		(2.120)	(2.437)
Constant	47.893***	46.653***	45.161***	46.653***	47.984***	49.083***
	(0.747)	(1.193)	(1.294)	(1.193)	(1.395)	(1.293)
Observations	3,010	1,490	1,520	1,014	1,002	994
R2	0.014	0.002	0.024	0.015	0.006	0.028
Adjusted R2	0.013	-0.002	0.021	0.012	0.003	0.025
Residual Std. Error	23.210	22.727	23.652	22.494	23.624	23.473
F Statistic	13.967***	0.530	7.531***	5.226***	2.152*	9.393***
Note:	p<0.1; p<0.05; p<0.01

:::

5.3 Table B.2

::: {#tbl-B-2 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E2 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E2 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E2 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E2",
            dep.var.caption = "Dependent variable: E2",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)

Linear regressions: Treatment effects on E2

	Dependent variable: E2
	full	confirmation	contradiction	point	interval	both
	(1)	(2)	(3)	(4)	(5)	(6)
contradiction	-1.235			-1.497	-1.578	-0.612
	(1.072)			(1.779)	(1.943)	(1.852)
both		1.558	2.443
		(1.806)	(1.825)
interval		0.174	0.092
		(1.867)	(1.858)
stage 2	3.535***	4.227***	-3.103**	4.227***	2.998**	3.348**
	(0.782)	(1.159)	(1.407)	(1.159)	(1.468)	(1.433)
contradiction x stage 2	-5.636***			-7.331***	-3.812*	-5.768***
	(1.125)			(1.824)	(1.977)	(2.059)
interval x stage 2		-0.879	0.683
		(1.843)	(2.041)
both x stage 2		-1.230	2.289
		(1.871)	(1.932)
Constant	50.681***	50.108***	48.611***	50.108***	50.282***	51.666***
	(0.757)	(1.250)	(1.265)	(1.250)	(1.387)	(1.304)
Observations	3,010	1,490	1,520	1,014	1,002	994
R2	0.013	0.007	0.005	0.023	0.008	0.010
Adjusted R2	0.012	0.004	0.002	0.020	0.005	0.007
Residual Std. Error	21.997	22.066	21.941	20.890	22.813	22.276
F Statistic	12.879***	2.214*	1.655	7.827***	2.700**	3.462**
Note:	p<0.1; p<0.05; p<0.01

:::

5.4 Table B.3

::: {#tbl-B-3 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E3 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E3 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E3 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E3",
            dep.var.caption = "Dependent variable: E3",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)

Linear regressions: Treatment effects on E3

	Dependent variable: E3
	full	confirmation	contradiction	point	interval	both
	(1)	(2)	(3)	(4)	(5)	(6)
contradiction	-0.317			1.231	0.356	-2.623
	(1.049)			(1.814)	(1.864)	(1.767)
both		4.813***	0.958
		(1.752)	(1.828)
interval		2.199	1.323
		(1.787)	(1.889)
stage 2	0.148	2.161	-5.425***	2.161	-0.006	-1.779
	(0.818)	(1.477)	(1.635)	(1.477)	(1.283)	(1.469)
contradiction x stage 2	-3.721***			-7.585***	-2.558	-0.971
	(1.217)			(2.204)	(1.972)	(2.136)
interval x stage 2		-3.940*	2.675
		(2.083)	(2.253)
both x stage 2		-2.167	2.861
		(1.956)	(2.217)
Constant	48.591***	46.278***	47.510***	46.278***	48.477***	51.091***
	(0.733)	(1.204)	(1.357)	(1.204)	(1.321)	(1.274)
Observations	3,010	1,490	1,520	1,014	1,002	994
R2	0.006	0.004	0.009	0.012	0.002	0.007
Adjusted R2	0.005	0.001	0.006	0.009	-0.001	0.004
Residual Std. Error	22.475	21.461	23.399	22.160	22.847	22.373
F Statistic	5.563***	1.320	2.815**	4.050***	0.678	2.489*
Note:	p<0.1; p<0.05; p<0.01

:::

5.5 Table B.4

::: {#tbl-B-4 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E12 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E12 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E12 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E12",
            dep.var.caption = "Dependent variable: E12",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)

Linear regressions: Treatment effects on E12

	Dependent variable: E12
	full	confirmation	contradiction	point	interval	both
	(1)	(2)	(3)	(4)	(5)	(6)
contradiction	-0.453			0.446	-1.087	-0.776
	(1.031)			(1.772)	(1.754)	(1.838)
both		1.672	0.451
		(1.760)	(1.850)
interval		2.072	0.539
		(1.743)	(1.783)
stage 2	1.817**	3.090**	4.460***	3.090**	-0.401	2.684*
	(0.828)	(1.365)	(1.611)	(1.365)	(1.499)	(1.440)
contradiction x stage 2	3.950***			1.370	5.012**	5.592***
	(1.198)			(2.112)	(2.050)	(2.065)
interval x stage 2		-0.406	3.816*
		(1.984)	(2.187)
both x stage 2		-3.491*	0.150
		(2.027)	(2.134)
Constant	58.358***	57.127***	57.573***	57.127***	59.200***	58.800***
	(0.716)	(1.230)	(1.276)	(1.230)	(1.234)	(1.258)
Observations	3,010	1,490	1,520	1,014	1,002	994
R2	0.010	0.004	0.019	0.008	0.007	0.021
Adjusted R2	0.009	0.001	0.015	0.005	0.004	0.018
Residual Std. Error	22.155	21.169	23.078	22.444	21.919	22.090
F Statistic	10.599***	1.214	5.751***	2.679**	2.266*	7.149***
Note:	p<0.1; p<0.05; p<0.01

:::

5.6 Table B.5

::: {#tbl-B-5 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E13 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E13 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E13 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E13",
            dep.var.caption = "Dependent variable: E13",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)

Linear regressions: Treatment effects on E13

	Dependent variable: E13
	full	confirmation	contradiction	point	interval	both
	(1)	(2)	(3)	(4)	(5)	(6)
contradiction	0.922			-2.686	4.258**	1.173
	(1.037)			(1.743)	(1.846)	(1.792)
both		0.479	4.338**
		(1.749)	(1.786)
interval		-1.358	5.586***
		(1.811)	(1.779)
stage 2	-1.549*	-1.333	4.212***	-1.333	0.068	-3.362**
	(0.873)	(1.414)	(1.579)	(1.414)	(1.581)	(1.543)
contradiction x stage 2	3.721***			5.546***	-0.192	5.848***
	(1.221)			(2.120)	(2.054)	(2.176)
interval x stage 2		-2.029	-1.726
		(2.093)	(2.201)
both x stage 2		1.401	-4.336**
		(2.121)	(2.052)
Constant	55.130***	55.414***	52.728***	55.414***	54.056***	55.893***
	(0.736)	(1.200)	(1.264)	(1.200)	(1.357)	(1.273)
Observations	3,010	1,490	1,520	1,014	1,002	994
R2	0.006	0.002	0.010	0.005	0.009	0.013
Adjusted R2	0.005	-0.001	0.007	0.002	0.006	0.010
Residual Std. Error	21.788	22.070	21.473	21.291	21.762	22.259
F Statistic	5.977***	0.717	3.041***	1.812	3.054**	4.266***
Note:	p<0.1; p<0.05; p<0.01

:::

5.7 Table B.6

::: {#tbl-B-6 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E23 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E23 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E23 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E23",
            dep.var.caption = "Dependent variable: E23",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)

Linear regressions: Treatment effects on E23

	Dependent variable: E23
	full	confirmation	contradiction	point	interval	both
	(1)	(2)	(3)	(4)	(5)	(6)
contradiction	1.513			0.254	1.193	3.086
	(1.154)			(2.075)	(1.963)	(1.959)
both		0.306	3.139
		(2.028)	(2.006)
interval		0.722	1.661
		(2.047)	(1.992)
stage 2	3.099***	3.308**	-7.562***	3.308**	2.074	3.893**
	(0.855)	(1.389)	(1.812)	(1.389)	(1.458)	(1.597)
contradiction x stage 2	-10.200***			-10.869***	-7.545***	-12.211***
	(1.326)			(2.283)	(2.198)	(2.417)
interval x stage 2		0.585	-0.756
		(2.116)	(2.564)
both x stage 2		-1.234	2.091
		(2.013)	(2.447)
Constant	59.658***	59.322***	59.575***	59.322***	60.043***	59.628***
	(0.829)	(1.452)	(1.481)	(1.453)	(1.442)	(1.416)
Observations	3,010	1,490	1,520	1,014	1,002	994
R2	0.020	0.005	0.027	0.026	0.012	0.024
Adjusted R2	0.019	0.001	0.023	0.023	0.009	0.021
Residual Std. Error	23.195	23.236	23.167	23.966	22.547	23.058
F Statistic	20.085***	1.441	8.284***	8.939***	3.966***	8.016***
Note:	p<0.1; p<0.05; p<0.01

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